240 lines
9.2 KiB
Python
240 lines
9.2 KiB
Python
|
# SPDX-License-Identifier: Apache-2.0
|
||
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||
|
|
import math
|
||
|
|
from collections import defaultdict
|
||
|
|
from dataclasses import dataclass, field
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from vllm.config import CacheConfig, VllmConfig
|
||
|
|
from vllm.logger import init_logger
|
||
|
|
from vllm.model_executor.layers.attention import Attention
|
||
|
|
from vllm.model_executor.models.interfaces import MultiModalEmbeddings
|
||
|
|
from vllm.model_executor.models.utils import extract_layer_index
|
||
|
|
from vllm.platforms import current_platform
|
||
|
|
from vllm.utils.mem_utils import MemorySnapshot, format_gib
|
||
|
|
from vllm.v1.attention.backend import AttentionBackend, AttentionMetadataBuilder
|
||
|
|
from vllm.v1.kv_cache_interface import KVCacheGroupSpec, KVCacheSpec
|
||
|
|
|
||
|
|
logger = init_logger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class AttentionGroup:
|
||
|
|
backend: type[AttentionBackend]
|
||
|
|
layer_names: list[str]
|
||
|
|
kv_cache_spec: KVCacheSpec
|
||
|
|
kv_cache_group_id: int
|
||
|
|
# When ubatching is enabled we will have a metadata builder for each ubatch
|
||
|
|
# so that if they use internal persistent buffers for cudagraphs, and they
|
||
|
|
# won't have to worry about conflicting with the other ubatches.
|
||
|
|
metadata_builders: list[AttentionMetadataBuilder] = field(
|
||
|
|
default_factory=lambda: []
|
||
|
|
)
|
||
|
|
|
||
|
|
def create_metadata_builders(
|
||
|
|
self,
|
||
|
|
vllm_config,
|
||
|
|
device,
|
||
|
|
kernel_block_size: int | None,
|
||
|
|
num_metadata_builders: int = 1,
|
||
|
|
):
|
||
|
|
kv_cache_spec_builder = (
|
||
|
|
self.kv_cache_spec.copy_with_new_block_size(kernel_block_size)
|
||
|
|
if kernel_block_size is not None
|
||
|
|
else self.kv_cache_spec
|
||
|
|
)
|
||
|
|
self.metadata_builders = [
|
||
|
|
self.backend.get_builder_cls()(
|
||
|
|
kv_cache_spec_builder,
|
||
|
|
self.layer_names,
|
||
|
|
vllm_config,
|
||
|
|
device,
|
||
|
|
)
|
||
|
|
for _ in range(num_metadata_builders)
|
||
|
|
]
|
||
|
|
|
||
|
|
def get_metadata_builder(self, ubatch_id: int = 0) -> AttentionMetadataBuilder:
|
||
|
|
assert len(self.metadata_builders) > ubatch_id
|
||
|
|
return self.metadata_builders[ubatch_id]
|
||
|
|
|
||
|
|
|
||
|
|
def sanity_check_mm_encoder_outputs(
|
||
|
|
mm_embeddings: MultiModalEmbeddings,
|
||
|
|
expected_num_items: int,
|
||
|
|
) -> None:
|
||
|
|
"""
|
||
|
|
Perform sanity checks for the result of
|
||
|
|
[`vllm.model_executor.models.SupportsMultiModal.embed_multimodal`][].
|
||
|
|
"""
|
||
|
|
assert isinstance(mm_embeddings, (list, tuple, torch.Tensor)), (
|
||
|
|
"Expected multimodal embeddings to be a list/tuple of 2D tensors, "
|
||
|
|
f"or a single 3D tensor, but got {type(mm_embeddings)} "
|
||
|
|
"instead. This is most likely due to incorrect implementation "
|
||
|
|
"of the model's `embed_multimodal` method."
|
||
|
|
)
|
||
|
|
|
||
|
|
assert len(mm_embeddings) == expected_num_items, (
|
||
|
|
"Expected number of multimodal embeddings to match number of "
|
||
|
|
f"input items: {expected_num_items}, but got {len(mm_embeddings)=} "
|
||
|
|
"instead. This is most likely due to incorrect implementation "
|
||
|
|
"of the model's `embed_multimodal` method."
|
||
|
|
)
|
||
|
|
|
||
|
|
assert all(e.ndim == 2 for e in mm_embeddings), (
|
||
|
|
"Expected multimodal embeddings to be a sequence of 2D tensors, "
|
||
|
|
f"but got tensors with shapes {[e.shape for e in mm_embeddings]} "
|
||
|
|
"instead. This is most likely due to incorrect implementation "
|
||
|
|
"of the model's `embed_multimodal` method."
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def request_memory(init_snapshot: MemorySnapshot, cache_config: CacheConfig) -> int:
|
||
|
|
"""
|
||
|
|
Calculate the amount of memory required by vLLM, then validate
|
||
|
|
that the current amount of free memory is sufficient for that.
|
||
|
|
"""
|
||
|
|
requested_memory = math.ceil(
|
||
|
|
init_snapshot.total_memory * cache_config.gpu_memory_utilization
|
||
|
|
)
|
||
|
|
|
||
|
|
if init_snapshot.free_memory < requested_memory:
|
||
|
|
raise ValueError(
|
||
|
|
f"Free memory on device {init_snapshot.device_} "
|
||
|
|
f"({format_gib(init_snapshot.free_memory)}/"
|
||
|
|
f"{format_gib(init_snapshot.total_memory)} GiB) on startup "
|
||
|
|
f"is less than desired GPU memory utilization "
|
||
|
|
f"({cache_config.gpu_memory_utilization}, "
|
||
|
|
f"{format_gib(requested_memory)} GiB). Decrease GPU memory "
|
||
|
|
f"utilization or reduce GPU memory used by other processes."
|
||
|
|
)
|
||
|
|
|
||
|
|
return requested_memory
|
||
|
|
|
||
|
|
|
||
|
|
def add_kv_sharing_layers_to_kv_cache_groups(
|
||
|
|
shared_kv_cache_layers: dict[str, str],
|
||
|
|
kv_cache_groups: list[KVCacheGroupSpec],
|
||
|
|
runner_only_attn_layers: set[str] | None = None,
|
||
|
|
) -> None:
|
||
|
|
"""
|
||
|
|
Sets up KV cache sharing by reusing the allocated KV caches in `kv_caches`
|
||
|
|
for layers that do not allocate its own KV cache, based on the mapping in
|
||
|
|
`shared_kv_cache_layers`. Adds these layers to the corresponding KV cache
|
||
|
|
group, which is needed to ensure that attention metadata is assigned later.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shared_kv_cache_layers: Layer pairings for cross-layer KV sharing.
|
||
|
|
If an Attention layer `layer_name` is in the keys of this dict, it
|
||
|
|
means this layer will perform attention using the keys and values
|
||
|
|
from the KV cache of `shared_kv_cache_layers[layer_name]`.
|
||
|
|
kv_cache_groups: The KV cache groups of the model.
|
||
|
|
"""
|
||
|
|
layer_to_kv_cache_group: dict[str, KVCacheGroupSpec] = {}
|
||
|
|
for kv_cache_group in kv_cache_groups:
|
||
|
|
for layer_name in kv_cache_group.layer_names:
|
||
|
|
layer_to_kv_cache_group[layer_name] = kv_cache_group
|
||
|
|
|
||
|
|
for layer_name, target_layer_name in shared_kv_cache_layers.items():
|
||
|
|
tgt_kv_cache_group = layer_to_kv_cache_group[target_layer_name]
|
||
|
|
tgt_kv_cache_group.layer_names.append(layer_name)
|
||
|
|
|
||
|
|
if runner_only_attn_layers is not None:
|
||
|
|
runner_only_attn_layers.add(layer_name)
|
||
|
|
|
||
|
|
|
||
|
|
def bind_kv_cache(
|
||
|
|
kv_caches: dict[str, torch.Tensor],
|
||
|
|
forward_context: dict[str, Attention],
|
||
|
|
runner_kv_caches: list[torch.Tensor],
|
||
|
|
num_attn_module: int = 1,
|
||
|
|
) -> None:
|
||
|
|
"""
|
||
|
|
Bind the allocated KV cache to both ModelRunner and forward context so
|
||
|
|
that the KV cache can be used in the forward pass.
|
||
|
|
|
||
|
|
This function:
|
||
|
|
1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
|
||
|
|
kv_caches.
|
||
|
|
2) Associates each attention layer in the `forward_context` with its
|
||
|
|
corresponding KV cache in kv_caches.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
kv_caches: The allocated kv_caches with layer names as keys.
|
||
|
|
forward_context: The global forward context containing all Attention
|
||
|
|
layers with layer names as keys.
|
||
|
|
runner_kv_caches: The kv_cache declared by ModelRunner.
|
||
|
|
"""
|
||
|
|
# Bind kv_caches to ModelRunner
|
||
|
|
assert len(runner_kv_caches) == 0
|
||
|
|
|
||
|
|
# Convert kv_caches dict to a list of tensors in the order of layer_index.
|
||
|
|
index2name = defaultdict(list)
|
||
|
|
for layer_name in kv_caches:
|
||
|
|
index2name[extract_layer_index(layer_name, num_attn_module)].append(layer_name)
|
||
|
|
|
||
|
|
for layer_index in sorted(index2name.keys()):
|
||
|
|
layer_names = index2name[layer_index]
|
||
|
|
if len(layer_names) > 1:
|
||
|
|
# One typical case is encoder-decoder model, e.g., bart.
|
||
|
|
# The cross attention and self attention in the same decoder layer
|
||
|
|
# has different layer_name but the same layer_index.
|
||
|
|
|
||
|
|
# TODO - analyze where runner_kv_caches is used and the right
|
||
|
|
# way to ensure it properly reflects multiple attention layers
|
||
|
|
# in the same decoder block.
|
||
|
|
if (
|
||
|
|
current_platform.is_cuda_alike()
|
||
|
|
or current_platform.is_xpu()
|
||
|
|
or current_platform.is_cpu()
|
||
|
|
):
|
||
|
|
# We know that the GPU / CPU runner is not impacted by this
|
||
|
|
# case. Some test code depends on runner_kv_caches, but
|
||
|
|
# not in a way that's impacted by ignoring this.
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
for layer_name in layer_names:
|
||
|
|
runner_kv_caches.append(kv_caches[layer_name])
|
||
|
|
|
||
|
|
# Bind kv_caches to forward context
|
||
|
|
for layer_name, kv_cache in kv_caches.items():
|
||
|
|
# NOTE: Use list because of v0 PP virtual engine.
|
||
|
|
forward_context[layer_name].kv_cache = [kv_cache]
|
||
|
|
|
||
|
|
|
||
|
|
def is_residual_scattered_for_sp(
|
||
|
|
vllm_config: VllmConfig, num_input_tokens: int
|
||
|
|
) -> bool:
|
||
|
|
"""Check if the residual tensor is scattered for sequence parallelism.
|
||
|
|
|
||
|
|
The residual tensor is scattered across tensor parallel ranks when sequence
|
||
|
|
parallelism and tensor parallelism is enabled.
|
||
|
|
|
||
|
|
This follows the same logic as SequenceParallelismPass.is_applicable_for_range():
|
||
|
|
- In full-graph compilation mode (no splitting ops or using inductor graph
|
||
|
|
partition), SP is always applied
|
||
|
|
- Otherwise, SP is only applied for specific shapes in compile_sizes
|
||
|
|
"""
|
||
|
|
if not vllm_config.compilation_config.pass_config.enable_sp:
|
||
|
|
return False
|
||
|
|
|
||
|
|
tp = vllm_config.parallel_config.tensor_parallel_size
|
||
|
|
|
||
|
|
if tp == 1:
|
||
|
|
return False
|
||
|
|
|
||
|
|
# When sequence parallelism is enabled, we always pad num_input_tokens
|
||
|
|
# to be a multiple of tensor_parallel_size (tp) earlier.
|
||
|
|
assert num_input_tokens % tp == 0
|
||
|
|
|
||
|
|
if (
|
||
|
|
not vllm_config.compilation_config.splitting_ops
|
||
|
|
or vllm_config.compilation_config.use_inductor_graph_partition
|
||
|
|
):
|
||
|
|
return True
|
||
|
|
compile_sizes = vllm_config.compilation_config.compile_sizes
|
||
|
|
if compile_sizes is None:
|
||
|
|
return False
|
||
|
|
return num_input_tokens in compile_sizes
|